一种快速分段变形的多模态图像配准方法

Girish Gopalakrishnan, S. Kumar, A. Narayanan, R. Mullick
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引用次数: 7

摘要

医学图像融合越来越流行,通过智能地“融合”从两个不同的图像中获得的信息来提高诊断的准确性。这些图像可以从不同时间实例的同一模态获得,也可以从记录互补信息的多个模态获得。由于人体的性质以及患者的运动和呼吸,在医学成像中需要可变形配准算法。典型的非参数(可变形)配准算法,如基于流体的、基于恶魔的和基于曲率的技术,计算量很大,并且仅用于单模态配准。我们提出了一种快速且可变形的算法,采用两层策略,其中基于全局mi的仿射配准之后是局部分段细化。我们已经在CT和PET图像上测试了该方法,并通过临床专家验证了该方法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fast piecewise deformable method for multi-modality image registration
Medical image fusion is becoming increasingly popular for enhancing diagnostic accuracy by intelligently 'fusing' information obtained from two different images. These images may be obtained from the same modality at different time instances or from multiple modalities recording complementary information. Due to the nature of the human body and also due to patient motion and breathing, there is a need for deformable registration algorithms in medical imaging. Typical nonparametric (deformable) registration algorithms such as the fluid-based, demons and curvature-based techniques are computationally intensive and have been demonstrated for mono-modality registrations only. We propose a fast and deformable algorithm using a 2-tiered strategy wherein a global MI-based affine registration is followed by a local piecewise refinement. We have tested this method on CT and PET images and validated the same using clinical experts
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